While these are supervised models that are pre-trained with unsupervised contrastive learning, an alternative setting in the previous literature is supervised contrastive learning. In this setting, labeled data is utilized (which we do not have in our applications) to construct positive and negative pairs for learning pixel level representations [1]}. In contrast, we directly use convolutional neural networks to learn pixel embeddings with pixel-level contrastive loss without labels. The segmentation is achieved through clustering, which makes our model totally unsupervised, unlike previous methods that incorporate labels at some point in the segmentation.
